Final Project

library(mosaic)
library(tidyverse)
library(lubridate)
library(DataComputing)
library(rvest)
library(broom)

Research Focus:

As COVID-19 spreads at an alarming rate, a pressing question at a global scale emerges– what factors of a country contribute to the spread of Coronavirus. We hope to analyze the relationship between population and average spread of COVID-19 across the six inhabeted continents.

Data Access

Reading in the Data:

Data Source 1: COVID

COVID <- read.csv(file = "total-covid-cases-deaths-per-million.csv")
COVID

COVID %>%
  nrow()

[1] 9487

COVID %>%
  names()

  [1] "total.covid.cases.deaths.per.million" "X"                                    "X.1"                                 
  [4] "X.2"                                  "X.3"                                  "X.4"                                 
  [7] "X.5"                                  "X.6"                                  "X.7"                                 
 [10] "X.8"                                  "X.9"                                  "X.10"                                
 [13] "X.11"                                 "X.12"                                 "X.13"                                
 [16] "X.14"                                 "X.15"                                 "X.16"                                
 [19] "X.17"                                 "X.18"                                 "X.19"                                
 [22] "X.20"                                 "X.21"                                 "X.22"                                
 [25] "X.23"                                 "X.24"                                 "X.25"                                
 [28] "X.26"                                 "X.27"                                 "X.28"                                
 [31] "X.29"                                 "X.30"                                 "X.31"                                
 [34] "X.32"                                 "X.33"                                 "X.34"                                
 [37] "X.35"                                 "X.36"                                 "X.37"                                
 [40] "X.38"                                 "X.39"                                 "X.40"                                
 [43] "X.41"                                 "X.42"                                 "X.43"                                
 [46] "X.44"                                 "X.45"                                 "X.46"                                
 [49] "X.47"                                 "X.48"                                 "X.49"                                
 [52] "X.50"                                 "X.51"                                 "X.52"                                
 [55] "X.53"                                 "X.54"                                 "X.55"                                
 [58] "X.56"                                 "X.57"                                 "X.58"                                
 [61] "X.59"                                 "X.60"                                 "X.61"                                
 [64] "X.62"                                 "X.63"                                 "X.64"                                
 [67] "X.65"                                 "X.66"                                 "X.67"                                
 [70] "X.68"                                 "X.69"                                 "X.70"                                
 [73] "X.71"                                 "X.72"                                 "X.73"                                
 [76] "X.74"                                 "X.75"                                 "X.76"                                
 [79] "X.77"                                 "X.78"                                 "X.79"                                
 [82] "X.80"                                 "X.81"                                 "X.82"                                
 [85] "X.83"                                 "X.84"                                 "X.85"                                
 [88] "X.86"                                 "X.87"                                 "X.88"                                
 [91] "X.89"                                 "X.90"                                 "X.91"                                
 [94] "X.92"                                 "X.93"                                 "X.94"                                
 [97] "X.95"                                 "X.96"                                 "X.97"                                
[100] "X.98"                                 "X.99"                                 "X.100"                               
[103] "X.101"                                "X.102"                                "X.103"                               
[106] "X.104"                                "X.105"                                "X.106"                               
[109] "X.107"                                "X.108"                                "X.109"                               
[112] "X.110"                                "X.111"                                "X.112"                               
[115] "X.113"                                "X.114"                                "X.115"                               
[118] "X.116"                                "X.117"                                "X.118"                               
[121] "X.119"                                "X.120"                                "X.121"                               
[124] "X.122"                                "X.123"                                "X.124"                               
[127] "X.125"                                "X.126"                                "X.127"                               
[130] "X.128"                                "X.129"                                "X.130"                               
[133] "X.131"                                "X.132"                                "X.133"                               
[136] "X.134"                                "X.135"                                "X.136"                               
[139] "X.137"                                "X.138"                                "X.139"                               
[142] "X.140"                                "X.141"                                "X.142"                               
[145] "X.143"                                "X.144"                                "X.145"                               
[148] "X.146"                                "X.147"                                "X.148"                               
[151] "X.149"                                "X.150"                                "X.151"                               
[154] "X.152"                                "X.153"                                "X.154"                               
[157] "X.155"                                "X.156"                                "X.157"                               
[160] "X.158"                                "X.159"                                "X.160"                               
[163] "X.161"                                "X.162"                                "X.163"                               
[166] "X.164"                                "X.165"                                "X.166"                               
[169] "X.167"                                "X.168"                                "X.169"                               
[172] "X.170"                                "X.171"                                "X.172"                               
[175] "X.173"                                "X.174"                                "X.175"                               
[178] "X.176"                                "X.177"                                "X.178"                               
[181] "X.179"                                "X.180"                                "X.181"                               
[184] "X.182"                                "X.183"                                "X.184"                               
[187] "X.185"                                "X.186"                                "X.187"                               
[190] "X.188"                                "X.189"                                "X.190"                               
[193] "X.191"                                "X.192"                                "X.193"                               
[196] "X.194"                                "X.195"                                "X.196"                               
[199] "X.197"                                "X.198"                                "X.199"                               
[202] "X.200"                                "X.201"                                "X.202"                               
[205] "X.203"                                "X.204"                                "X.205"                               
[208] "X.206"                                "X.207"                                "X.208"                               
[211] "X.209"                                "X.210"                                "X.211"                               
[214] "X.212"                                "X.213"                                "X.214"                               
[217] "X.215"                                "X.216"                                "X.217"                               
[220] "X.218"                                "X.219"                                "X.220"                               
[223] "X.221"                                "X.222"                                "X.223"                               
[226] "X.224"                                "X.225"                                "X.226"                               
[229] "X.227"                                "X.228"                                "X.229"                               
[232] "X.230"                                "X.231"                                "X.232"                               
[235] "X.233"                                "X.234"                                "X.235"                               
[238] "X.236"                                "X.237"                                "X.238"                               
[241] "X.239"                                "X.240"                                "X.241"                               
[244] "X.242"                                "X.243"                                "X.244"                               
[247] "X.245"                                "X.246"                                "X.247"                               
[250] "X.248"                                "X.249"                                "X.250"                               
[253] "X.251"                                "X.252"                                "X.253"                               
[256] "X.254"                               

COVID %>%
  head()

Data Source 2: CountryData

CountryData

CountryData %>%
  nrow()

[1] 256

CountryData %>%
  names()

 [1] "country"           "area"              "pop"               "growth"            "birth"             "death"            
 [7] "migr"              "maternal"          "infant"            "life"              "fert"              "health"           
[13] "HIVrate"           "HIVpeople"         "HIVdeath"          "obesity"           "underweight"       "educ"             
[19] "unemploymentYouth" "GDP"               "GDPgrowth"         "GDPcapita"         "saving"            "indProd"          
[25] "labor"             "unemployment"      "family"            "tax"               "budget"            "debt"             
[31] "inflation"         "discount"          "lending"           "narrow"            "broad"             "credit"           
[37] "shares"            "balance"           "exports"           "imports"           "gold"              "externalDebt"     
[43] "homeStock"         "abroadStock"       "elecProd"          "elecCons"          "elecExp"           "elecImp"          
[49] "elecCap"           "elecFossil"        "elecNuc"           "elecHydro"         "elecRenew"         "oilProd"          
[55] "oilExp"            "oilImp"            "oilRes"            "petroProd"         "petroCons"         "petroExp"         
[61] "petroImp"          "gasProd"           "gasCons"           "gasExp"            "gasImp"            "gasRes"           
[67] "mainlines"         "cell"              "netHosts"          "netUsers"          "airports"          "railways"         
[73] "roadways"          "waterways"         "marine"            "military"         

CountryData %>%
  head()

Data Source 3: countryRegions

countryRegions

countryRegions %>%
  nrow()

[1] 254

countryRegions %>%
  names()

 [1] "ISO3"         "ADMIN"        "REGION"       "continent"    "GEO3major"    "GEO3"         "IMAGE24"      "GLOCAF"      
 [9] "Stern"        "SRESmajor"    "SRES"         "GBD"          "AVOIDnumeric" "AVOIDname"    "LDC"          "SID"         
[17] "LLDC"        

countryRegions %>%
  head()

Data Wrangling

Tidying the COVID Dataset

COVID

Since our analysis is focused on the spread of COVID-19, we select only columns which pertain to the number of COVID-19 cases in countries over time.

TidyCOVID <- COVID %>%
  rename(country = total.covid.cases.deaths.per.million ) %>%
  rename( Code = X ) %>%
  rename(date = X.1 ) %>%
  rename(casesPerMillion = X.3) %>%
  filter(row_number() > 1) %>%
  subset(select = c(1,2,3,5)) %>%
  mutate( country = as.character(country) ) %>%
  mutate(date = mdy(date)) %>%
  mutate(casesPerMillion = as.integer(casesPerMillion) - 1)

TidyCOVID

EVELYN pls explain what an instance represents

Wrangling of countryRegions Dataset

We will extract the ISO3 country code and continent from the countryRegions data. Since naming conventions of countries is variate, the ISO3 country code allows us a standardized demarcation of country with which to join with other data tables.

Labels <-
  countryRegions %>%
  subset(select = c("ISO3", "REGION")) %>%
  rename(continent = REGION)
Labels

Data Extraction of CountryData Dataset

We will select the aspects of CountryData relevant to our analysis. These attributes are: area (sq km) and pop (number of people).

RelevantCountryData <-
  CountryData %>%
  subset(select = c(1,2,3)) %>%
  mutate(popdensity = pop/area)
RelevantCountryData

Joining Data & Relevant Variable Synthesis

Calculate the number of cases in each country by multiplying casesPerMillion by population (in millions). This variable is now a standardized metric with which we can compare countries.

COVIDGrowth <-
  inner_join(TidyCOVID, RelevantCountryData, by = c("country")) %>%
  mutate("cases" = (casesPerMillion * round(pop/1000000, digits = 0)))
COVIDGrowth <-
  COVIDGrowth %>%
  left_join(Labels, by = c("Code" = "ISO3"))

Column `Code`/`ISO3` joining factor and character vector, coercing into character vector

COVIDGrowth

Creation of new Data Table: FirstInstance

This table records the first date that a country recorded a nonzero number of COVID-19 cases. This datagraph will help us visualize /bwhen/ countries first became infected.

FirstInstance <-
  COVIDGrowth %>%
  filter(cases != 0) %>%
  group_by(country, continent) %>%
  summarise(beginningofspread = min(date))
  
FirstInstance

This table averages the number of case increase per day from the first day a country had COVID-19 to the most recent in the data table (April 5 2020)

DailySpread <-
  left_join(COVIDGrowth, FirstInstance, by = c("country")) %>%
  filter(date == "2020-04-05") %>%
  mutate(dayselapsed = date - beginningofspread) %>%
  mutate(dailyspread = cases / as.numeric(dayselapsed) ) %>%
  mutate(dailyspreadpermillion = casesPerMillion / as.numeric(dayselapsed) ) %>%
  subset(select = c("country", "beginningofspread", "dailyspread", "dailyspreadpermillion"))
DailySpread$dailyspread[is.na(DailySpread$dailyspread)] <- 0
DailySpread

COVIDFinal <-
  left_join(COVIDGrowth, DailySpread, by = c("country"))

COVIDFinal

Readible Version– Wide Countries

WideCountries <-
  COVIDFinal %>%
  subset(select = c("country", "date", "cases")) %>%
  spread(key = date, value = cases)
WideCountries[is.na(WideCountries)] <- 0
WideCountries

Data Visualization

COVIDFinal %>%
  group_by(date) %>%
  summarise(totalcases = sum(cases)) %>%
  ggplot(aes(x = date, y = totalcases)) + 
  geom_point() +
  xlab("Date") +
  ylab("COVID-19 Cases")

na.omit(COVIDFinal) %>%
  group_by(date, continent) %>%
  summarise(totalcases = sum(cases)) %>%
  ggplot(aes(x = date, y = totalcases)) + 
  geom_point() +
  facet_wrap(~continent) +
  xlab("Date") +
  ylab("COVID-19 Cases")

  
COVIDFinal %>%
  group_by(country) %>%
  summarise(dailyspread = mean(dailyspread)) %>%
  arrange(desc(dailyspread)) %>%
  head(20) %>%
  ggplot(aes(x = reorder(country, desc(dailyspread)), y= dailyspread)) +
  geom_bar(stat="identity", position = 'stack', width=.9) +
  theme(axis.text.x=element_text(angle = 60, hjust = 1)) +
  scale_y_continuous(labels = function(x) format(x, scientific = FALSE)) +
  ylab("Average Number Infected Per Day") +
  theme(axis.title.x = element_blank())

na.omit(COVIDFinal) %>%
  ggplot(aes(x = pop, y = dailyspread, color = continent)) + 
  geom_point() +
  xlab("Population of Country") +
  ylab("Average Number Infected Per Day") +
  stat_smooth(method = lm) 

WITHOUT OUTLIERS SHOWS STILL STRONG POSITIVE CORRELATION

na.omit(COVIDFinal) %>%
  ggplot(aes(x = pop, y = dailyspread, color = continent)) + 
  geom_point() +
  xlim(0,500000000) +
  ylim(0, 40000) +
  xlab("Population of Country") +
  ylab("Average Number Infected Per Day") +
  stat_smooth(method = lm) 

na.omit(FirstInstance) %>%
  ggplot(aes(x = beginningofspread, fill = continent)) +
  geom_dotplot(stackgroups = TRUE, binwidth = 1, binpositions="all") +
  xlab("Country's First Case of COVID-19") +
  theme(panel.background = element_blank(),
        axis.text.y = element_blank(),
        axis.ticks.y = element_blank(),
        axis.title.y = element_blank())